The contribution of greenhouse gases to the recent slowdown in global-mean temperature trends

We use a simple global-mean energy balance model (Hansen et al 1981, Shine and Highwood 2002) which consists of two boxes, one representing the atmosphere and ocean mixed layer (with assumed depth 100 m), and the other representing the deep ocean (with assumed depth 900 m) (see inset in figure 1). The changes in mixed layer temperature are taken to be the anomaly in GMST. The mixed layer is subject to a number of radiative forcing RF_i the sum over which yields the total radiative forcing $\begin{array}{ccc}{\rm{R}}{\rm{F}} & = & {\rm{\Sigma }}{{\rm{R}}{\rm{F}}}_{i}\end{array}$. The mixed layer and the deep ocean exchange energy via diffusion, assumed to be proportional to the difference between the GMST and the deep ocean temperature. Supplementary Data section S4 and table S4 provides more information on the model parameters used here. The climate system response is characterised by a climate sensitivity parameter λ and we explore the sensitivity of our results to the assumed value of λ over the range 0.3–1.05 K (W m⁻²)⁻¹. The performance of such a simple model in simulating historical temperature trends is discussed in section 3.1. We use the HadCRUT4.4 GMST dataset (Morice et al 2012 and www.metoffice.gov.uk/hadobs/hadcrut4/) for comparison and analysis.

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The first step is to demonstrate that the simple model produces reasonable results, compared to Earth system models. For this purpose, we use all forcings (including the natural components) from the RCP4.5 scenario of Meinshausen et al (2011), which will approximate the historical RFs in the CMIP5 models.

To then isolate the roles of individual GHG components, we produce an updated series of RF for the period 1850–2014. The CO₂ RFs are from table AII1.2 of IPCC (2013a); because IPCC (2013a) does not provide separate RFs for other WMGHGs, we instead use Meinshausen et al (2011) for the period up to 2003, and then use the global-mean data from the Advanced Global Atmospheric Gases Experiment (AGAGE) (Prinn et al 2000) available at http://agage.eas.gatech.edu/data_archive/global_mean/. This includes time series of methane, nitrous oxide, 16 ODSs and 9 other fluorinated GHGs. We update the CO₂ RF to 2014 using global-mean concentrations from the NOAA data (http://esrl.noaa.gov/gmd/ccgg/trends/global.html). The RF for CO₂, methane and nitrous oxide is calculated from the concentrations and the standard expressions given in table 6.2 of Ramaswamy et al (2001). For all other WMGHGs, the radiative efficiencies are taken from table 8.A.1 of Myhre et al (2013). For tropospheric and stratospheric ozone and stratospheric water vapour due to methane oxidation, the values from table AII1.2 of IPCC (2013a) are used directly, and assumed constant between 2011 and 2014; this assumption has only a minor impact on the results presented here.

The supplementary data section S1 provides full information on the merged data set, including the list of gases included and the radiative efficiencies used for the fluorinated gases, as well as figures showing the time evolution. Supplementary Data section S2 provides results from an alternative data set derived from the NOAA Global Monitoring Division network (http://esrl.noaa.gov/gmd/) for comparison purposes. Supplementary Data section S3 presents the simple expressions used to derive the RFs.

We first compare results from our simple model with observations and more complex climate models (figure 1) to establish that it is a useful tool for the purposes here, prior to the assessment of the role of individual GHGs in section 3.2. For this, we include all (anthropogenic and natural) RFs from Meinshausen et al (2011). As well as comparing against the observed HadCRUT4.4 data, we also use results from CMIP5 integrations. We present the smoothed average from 42 CMIP5 models, or model variants, using the first ensemble member (r1i1p1) of each model's RCP4.5 simulation (IPCC 2013b) focusing on the historical period. Figure 1 shows that the simple model broadly reproduces the observed GMST anomaly given by HadCRUT4.4 except for the high-frequency variability. The simple model reproduces both the hiatus between 1945 and 1960 and the slowdown around the year 2000. The simple model also broadly reproduces the results from CMIP5, although after 2000 CMIP5 systematically overestimates the GMST anomaly compared to HadCRUT4.4, as also shown in Fyfe et al (2016). The lower frequency oscillations of CMIP5 models between 1960 and 2000 are very similar to the simple model, indicating a similar response to the Pinatubo and El Chichón volcano eruptions. Finally, the uncertainty in the simple model due to the value of λ is similar to the 10%–90% range of the CMIP5 uncertainty shown by Fyfe et al (2016). These results demonstrate that the simple model is a useful tool for assessing the different greenhouse gas contributions to the recent slowdown.

Fyfe et al (2016) employed a method to demonstrate the slowdown based on overlapping trends of GMST anomaly with window sizes from 15 to 50 years. This method allows us to deal with the different timescales of the forcings, and at the same time avoid an arbitrary definition of when the slowdown begins and ends. Here we compute linear trends in both observed and simple-model GMST over a given window, with the value of that trend assigned to the middle of the window.

Figure 2 shows the GMST decadal trends for the GHGs contributions for a 15 year window size from the simple model, for a mid-range climate sensitivity parameter (0.75 K (W m⁻²)⁻¹). The observed trend is shown for reference. The largest contributor to the total GHG-forced temperature trend (figure 2(a)) is CO₂, with values exceeding 0.1 K decade⁻¹ in the early 1980s; in the 1985–2003 period (shaded box on figure 2(a)) it contributes to a steady or slightly increasing GMST trend. Despite this, figure 2(a) shows that, as a whole, the GHGs cause a reduction in warming trend, from 0.21 to 0.17 K decade⁻¹, over this same period. The simulated reduction in trend was found to be relatively insensitive to the choice of λ, with the decrease ranging from 0.03 to 0.05 K decade⁻¹.

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Figure 2(b) shows the individual contributors to GMST trends for groups of non-CO₂ GHG forcings (methane, ODSs, nitrous oxide, and non-ozone-depleting fluorinated gases (such as the hydrofluorocarbons) and stratospheric ozone). ODSs contribute the highest GMST trends during the entire period, but even at their mid-1980s peak, the trend is about a factor of two lower than that due to CO₂ (figure 2(a)). Nevertheless, ODSs are the dominant contributor to the GHG-induced slowdown, varying from 0.05 K decade⁻¹ in the late 1980s to 0.01 K decade⁻¹ by 2005; this contribution is slightly offset by the reduction in the negative RF due to the associated slow-down and reversal in stratospheric ozone trends. Although somewhat smaller, the methane contribution to the slowdown is still marked and is accentuated slightly by the related weakening of the growth rate in stratospheric water vapour from methane oxidation.

Figure 2(c) groups together the ODSs and stratospheric ozone, methane and the associated stratospheric water vapour changes, and also shows the effect of tropospheric ozone. The ODSs plus stratospheric ozone depletion cause a reduction in the smoothed GMST trend between 1985 and 2003 by about 0.026 K decade⁻¹, methane and stratospheric water vapour cause a reduction of 0.022 K decade⁻¹, while tropospheric ozone causes a reduction of about 0.016 K decade⁻¹; the sum of these (about 0.07 K decade⁻¹) is a substantial fraction of the mean observed trend (about 0.2 K decade⁻¹) over this period. It is offset by the increasing trend due to CO₂, nitrous oxide and other fluorinated gases, but the total GHG effect is still a reduction in the trend by about 0.04 K decade⁻¹. Our results hence do not support the conclusion of Pretis and Allen (2013) that the methane effect is small; the slowdown in methane growth rates are a significant contributor to the GHG-induced slowdown in GMST, and are only slightly smaller than the influence of the ODSs, when the offsetting effect of stratospheric ozone depletion is accounted for. Supplementary Data table S2 shows the values of the smoothed GMST trend for each component for the range of climate sensitivities used here.

In order to illustrate the dependence of the analysis on the choice of overlapping window size and λ, we employ a simple metric of the slowdown. This is defined as the change in GMST trend (2003 minus 1985), divided by the observed-mean GMST trend averaged over the same period. Hence, a positive value indicates a higher GMST trend in 2003 than 1985, and a negative value a lower GMST trend; the size gives a sense of the importance of the change relative to the total observed GMST trend. We are not attempting to attribute the observed trend (or change in trends) to particular forcings; there are many forced and unforced contributors to the observed trend, as well as uncertainty in the simple model parameters, particularly climate sensitivity. The metric is intended to illustrate the importance of different drivers of GMST trends, and to explore the dependence of these drivers to changing values in the climate sensitivity parameter, as well as in the 'window' that is used in the smoothing.

Figure 3 shows the effect for different GHG drivers, and their total, for window sizes between 5 and 20 years and λ between 0.3 and 1.4 K (W m⁻²)⁻¹. It shows a weak dependence on window size, and the expected monotonic dependence on climate sensitivity. Because the Montreal Protocol caused a relatively abrupt change in the time variation of the ODS forcing, figure 3 shows that it is most sensitive to the choice of window size.

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The changes in the constituent trends of the three groupings (methane plus stratospheric water vapour, ODSs plus stratospheric ozone, and tropospheric ozone) which contribute to the slowdown in GMST trends (figures 3(c)–(e)) can each cause slowdowns that reach or exceed 10% of the observed trend. CO₂ (figure 3(a)) causes an increasing trend, which is again about 5%–10%, and roughly equal but opposite to the effect of tropospheric ozone, while N₂O and the other fluorinated gases (figure 3(b)) cause a change which is only about 1%–2% of the observed trend. Figure 3(f) shows that for all window sizes and values of λ explored here, the GHGs together decrease the observed GMST trend in this period by between about 18% and 25%.